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- W2055099632 abstract "The prediction of critical points of thermodynamic systems is an important tool for modeling many high-pressure processes of theoretical and practical interest. In this article, the calculation of critical points of multicomponent mixtures is treated as a global minimization problem of a modified merit function associated with the criticality conditions obtained from the Gibbs tangent plane criterion, designed to discriminate the scale of the problem. The methodology used to solve the optimization problem is based on two versions of the particle swarm optimization (PSO), equipped with low-discrepancy sequences to prevent the sensitivity of the swarm with respect to the location of the initial population. To avoid a rapid decrease in the weight inertia, and to prevent stagnations near undesirable local minimizers, we present a modification of the PSO method, which uses different search cycles with the same inertia weight. This new version developed here is a fast and robust algorithm for solving the critical-point problem, via global optimization." @default.
- W2055099632 created "2016-06-24" @default.
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- W2055099632 date "2014-09-10" @default.
- W2055099632 modified "2023-10-07" @default.
- W2055099632 title "Computation of Critical Points of Mixtures Using Particle Swarm Optimization with Low-Discrepancy Sequences" @default.
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- W2055099632 doi "https://doi.org/10.1080/00986445.2014.947365" @default.
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